Finding Your Way To R

We think R is a great place to start your data science journey because it is an environment designed for data science. R is not just a programming language, but it is also an interactive ecosystem including a runtime, libraries, development environments, and extensions. All these features help you think about problems as a data scientist, while supporting fluent interaction between your brain and the computer.

However, after more than 25 years of development, the R ecosystem can seem overwhelming to newcomers. Whether you are just beginning R or have many years of data science experience, R offers a plethora of choice. Yet, when RStudio asks students about their biggest challenges in learning R, respondents overwhelmingly answer that survey question with another question: where should they begin?

Most journeys begin with a map. Here’s ours:

map from start to keep going!

We have created three tracks to help learners navigate the R ecosystem. These tracks are not meant to be exhaustive, but instead are designed to help you become productive in the minimum amount of time, based on your experience level.

Choose your learning path

Get started with the Tidyverse and R Markdown. No one starting point will serve all beginners, but here are 6 ways to begin learning R. Read more ...
Expand your R skills. Here are some common areas that people who already have some experience in R find particularly rewarding to learn. Read more ...
Go deep. Learning some topics in depth will both help you develop better code and share it more effectively with others. Read more ...
Grab a cheat sheet for your favorite package to help jog your memory for key functions while you work.
The RStudio team writes books to help you use our tools. Most have links to free online versions so you can study up right now.
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